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The basic idea of this paper comes from the paper published in American Political Science Review in 2022, Education or Indoctrination? The Violent Origins of Public School Systems in an Era of State-Building. Paglayan (2022) argues that states, in particular non-democracies, experienced mass violence, such as civil war, are more likely to install educational system for the children to enhance their compliance to the regime. Thus, the study examines the relationship between regime type, civil war experience, and education expansion in Latin America.

However, there are several points in Paglayan (2022) that are unresolved or unclear. First, do authoritarian regimes use indoctrination to protect children from potential mass violations following civil war? So, what are the criteria for determining the target of indoctrination? How do autocracies use indoctrination strategically to achieve their goals? Second, is school enrollment a good indicator of indoctrination?

Although the article contains insightful arguments, my question focuses on education and indoctrination. Is it true that they are mutually exclusive? Autocrats can use education to increase compliance by extending it to their children. However, it can also provide an opportunity for children to grow in their own abilities. Thus, I believe that what distinguishes education from indoctrination is determined on the demand side rather than the supply side. If the system’s beneficiaries (or indoctrinated) children have developed their capability, driving the country forward, even indoctrination strategy works as an education.

Theoretical Expectation

Education and indoctrination, I contend, are not mutually exclusive. The outcomes of the strategy are what justify education or indoctrination. For example, if the goal of an indoctrination strategy is to improve students compliance, it is less likely to provide diverse content and curriculum to improve students’ creativity. Otherwise, if such benefits are provided, even indoctrination will encourage students to have highly professionalized and developed skills, and technologies, which can have an productive impact on the entire regime.

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Therefore, here, I expect the relationship between level of indoctrination, education expenditure, economic development, and intellectual property of the state, which cannot be obtained without well-designed education.

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Hypothesis 1: Higher indoctrination leads to less advanced technological developments than higher education because indoctrination emphasizes increasing citizens’ compliance with the regime.

Hypothesis 2a: Indoctrination increases low-level technological developments while decreasing high-level technological developments.

Hypothesis 2b: Indoctrination increases technological developments, but decreases diversity of technological developments.

Hypothesis 3: The effect of indoctrination on technological developments is conditional on the economic capacity of a state.

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Preliminary Exploration

Intellectual property

To measure the intellectual property, I use the country-year patent data from 1995 to 2023 from World Intellectual Property Organization. What I use is the patent publication data, which are measured as total count by applicant’s origin by technology.

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I transformed the data into country-year structure, manipulating COW country codes. Then, I removed the observations that do not have COW country codes from the sample.

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In the sample, WIPO provides Field of technology that the published patents should belong to. There are 35 different technologies of patents in the sample. I rely on Concept of a Technology Classification for Country Comparisons, a report published in WIPO.

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Technology classification

I used a combination of different criteria to classify the technologies into low-tech, middle-level techs, and high-techs. These criteria include:

Complexity and sophistication of the technology: Generally, the more complex and sophisticated the technology is, the higher the level of tech it belongs to.
Level of innovation and research and development (R&D) involved: High-techs are typically associated with cutting-edge innovation and intensive R&D activities, while low-techs are often considered more mature and established technologies.
Economic impact and industrial relevance: High-techs are often associated with strategic industries ## and high-value-added products, while low-techs are often associated with more traditional and low-value-added sectors.

Based on these criteria, I classified the technologies into three broad categories as follows:

Low-tech: These are technologies that are relatively simple, mature, and established, and typically involve low levels of innovation and R&D. They are often associated with traditional and low-value-added sectors such as construction, transportation, and textiles.
Middle-level techs: These are technologies that are more complex and sophisticated than low-techs but not as cutting-edge and innovative as high-techs. They typically involve moderate levels of innovation and R&D and are associated with a wide range of sectors, such as manufacturing, healthcare, and logistics.
High-techs: These are technologies that are characterized by high levels of innovation and R&D, cutting-edge research and development, and strategic importance for the economy. They are typically associated with high-value-added industries such as biotechnology, information technology, and telecommunications.

You can find the sample of classified technologies of n = 1,000:

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Technology Classification

I used a combination of different criteria to classify the technologies into low-tech, middle-level techs, and high-techs. These criteria include followings:

Complexity and sophistication of the technology: Generally, the more complex and sophisticated the technology is, the higher the level of tech it belongs to.
Level of innovation and research and development (R&D) involved: High-techs are typically associated with cutting-edge innovation and intensive R&D activities, while low-techs are often considered more mature and established technologies.
Economic impact and industrial relevance: High-techs are often associated with strategic industries and high-value-added products, while low-techs are often associated with more traditional and low-value-added sectors.

Based on these criteria, I classified the technologies into three broad categories as follows:

Low-tech: These are technologies that are relatively simple, mature, and established, and typically involve low levels of innovation and R&D. They are often associated with traditional and low-value-added sectors such as construction, transportation, and textiles.
Middle-level techs: These are technologies that are more complex and sophisticated than low-techs but not as cutting-edge and innovative as high-techs. They typically involve moderate levels of innovation and R&D and are associated with a wide range of sectors, such as manufacturing, healthcare, and logistics.
High-techs: These are technologies that are characterized by high levels of innovation and R&D, cutting-edge research and development, and strategic importance for the economy. They are typically associated with high-value-added industries such as biotechnology, information technology, and telecommunications.

Trends in Technology Patents in Numbers

Number of the patent by different levels of technology (yearly)

Using the country-year units of patent data set, I manipulate three-scale measurements of classification of technologies. In the raw number of patents for different levels of technologies by year, we can observe that the number of patents for high-level technologies has increased drastically than other levels of technologies.

Trends in Technology Patents in Diversities

Following that, I manipulate a variable called diversity to demonstrate how different types of patents have been published over time. The majority of regions show an increasing trend in the diversity of technology patents, but Sub-Saharan Africa has little variation, implying that it is lagging behind in technological development. Also, Western European countries and North America have the greatest diversities in patents over time. It makes me think the possible technological inequalities.

When I disaggregate the patents into different levels of technologies, it clarifies that sub-Saharan Africa is underdevelopment in technologies regardless of their levels. Also, the increasing trends are likely to be driven by high-level technologies across different regions.

Diversity of the patent of technology by different region (yearly)

Diversity of the patent of technology by different levels and region (yearly)

Sample

Merge Total Number, Diversities, and Levels of Patents Technologies

The unit of analysis is country-year. I merged the patent_long_sum and patent_diversity_long_tech so that sample has two variables: sum_tech_patent and diversity.

sum_tech_patent: The total number of patents published in a given year and country by different levels of technology.
diversity: The number of areas in which patents have been published in a given year and country by different levels of technology.

Thus, the baseline sample of patents in technology has five variables including sum_tech_patent and diversity.

ccode: COW code for the country names.
year: It ranges between 1995 and 2022.
Class: three-scale measurements of Low-tech, Middle-tech, and High-tech.

As the sample data includes class variable for the same country-year pairs, the number of observations is 13804.

Indoctrination

I merge the sample with other data sets from V-Indoc and V-Dem to include variables on indoctrination for the analyses.

From V-Indoc data set, I select five variables to capture indoctrination for a preliminary analysis.
- v2xed_ed_inpt: Indoctrination potential in education. The potential of regimes to successfully indoctrinate through education is based on their control over the structures and processes of the education system. The index is a function of the coherence of the regime’s doctrine (whether it be democratic or autocratic) and the effort devoted to political education. Greater coherence and political education efforts are expected to generate higher potential for indoctrination. V-Indoc estimates the index by averaging two indices: v2xed_ed_poed and v2xed_ed_inco.
- v2xed_ed_poed: Political education effort in education. This index measures the extent to which the regime attempts to teach its core political values and ideologies through education based on political education in primary and secondary schools, and the teaching of a dominant ideology in the history curriculum.
  - V-Indoc estimates this index by taking the point estimates from a Bayesian factor analysis model of the indicators: v2edpoledprim, v2edpoledsec, and v2edideol.
  - v2edpoledprim: Political education, primary school
  - v2edpoledsec: Political education, secondary school
  - v2edideol: Ideology in the curriculum
- v2xed_ed_inco: Indoctrination coherence in education. This index measures the extent to which a coherent single doctrine of political values and model citizenship is known and promoted by educational agents. The index is a function of the centralization of the education system and the regime’s control over educational agents. Greater centralization and control are expected to lead to a more coherent doctrine being taught through education.
  - V-Indoc estimates the index by averaging two indices: v2xed_ed_cent and v2xed_ed_ctag.
  - v2xed_ed_cent: Centralization of the education system. This index measures the extent to which the regime has control over education structures and resources based on the centralization of the curriculum and textbooks.
  - V-Indoc estimates the index by averaging two indicators: v2edcentcurrlm and v2edcenttxbooks.
    - v2edcentcurrlm: Centralized curriculum. The official curriculum may only be a framework, to which individual schools can contribute. For this question, we are interested in all school subjects across levels of primary and secondary public education. If there are substantive differences between the primary and secondary education levels, please provide the response that is most accurate for the majority of schools. A national (or federal) authority can include a state body organized under the auspices of a Ministry of Education. The sub-national level includes states, provinces, districts, municipalities, villages, local educational authorities, etc.
    - v2edcenttxbooks: Centralized textbook approval. For this question, we are interested in core subjects, such as languages, mathematics, science, arts, social studies, history, geography. We are not interested in textbooks teaching foreign languages that could be subcontracted to a foreign publisher. Please consider school subjects across levels of formal primary and secondary public education. If there are substantive differences between the primary and secondary education levels, please provide the response that is most accurate for the majority of schools. Examples of ways in which textbook production is centrally approved or authorized include: a national public authority reviews textbook content and approves textbooks for use in schools; there is a state-mandated national list of textbooks that schools are recommended to use; the Ministry of Education directly publishes textbooks. A national (or federal) authority can include a public authority organized under the auspices of a Ministry of Education or a different authority.
  - v2xed_ed_ctag: Control over educational agents. This index measures the extent to which the regime is able to control teachers and teaching practices inside the classroom based on the strength of teacher autonomy and unions, and the hiring/firing of teachers.
  - Etimates the index by taking the point estimates from a Bayesian factor analysis model of the indicators: v2edteautonomy, v2edteunionindp, v2edtehire, and v2edtefire.
    - v2edteautonomy: Teacher autonomy in the classroom
    - v2edteunionindp: Independent teacher unions
    - v2edtehire: Political teacher hiring
    - v2edtefire: Political teacher firing
- v2edmath: What proportion of instructional weekly hours is dedicated to mathematics and natural sciences in primary education? For this question, please approximate the proportion of instructional hours across grades of primary education. Mathematics includes arithmetic, geometry, algebra, calculus. Natural sciences include chemistry, biology, physics, as well as classes in computing and engineering.

I think v2xed_ed_inco is a more relevant measurement to capture the relationship between indoctrination, economic and intellectual property development in theory. I would like to look into v2xed_ed_inco and its sub-indicators because I expect that indoctrination is more likely to enhance compliance and less likely to improve diversity or individual capability.

Other Covariates

Political and Economic Factors

I use V-Dem data set to include various political and economic variables.

v2x_polyarchy: Electoral democracy index. The electoral principle of democracy seeks to embody the core value of making rulers responsive to citizens, achieved through electoral competition for the electorate’s approval under circumstances when suffrage is extensive; political and civil society organizations can operate freely; elections are clean and not marred by fraud or systematic irregularities; and elections affect the composition of the chief executive of the country. In between elections, there is freedom of expression and an independent media capable of presenting alternative views on matters of political relevance. In the V-Dem conceptual scheme, electoral democracy is understood as an essential element of any other conception of representative democracy — liberal, participatory, deliberative, egalitarian, or some other.
v2x_libdem: Liberal democracy index. The liberal principle of democracy emphasizes the importance of protecting individual and minority rights against the tyranny of the state and the tyranny of the majority. The liberal model takes a “negative” view of political power insofar as it judges the quality of democracy by the limits placed on government. This is achieved by constitutionally protected civil liberties, strong rule of law, an independent judiciary, and effective checks and balances that, together, limit the exercise of executive power. To make this a measure of liberal democracy, the index also takes the level of electoral democracy into account.
e_boix_regime: Democracy (BMR). Dichotomous democracy measure based on contestation and participation. Countries coded democratic have (1) political leaders that are chosen through free and fair elections and (2) a minimal level of suffrage.
e_gdppc: GDP per capita. Point estimate from latent variable model of Gross Domestic Product Per Capita based on a number of sources.
e_gdp: GDP. Point estimate from latent variable model of Gross Domestic Product based on a number of sources.
e_pop: Population.
e_total_resources_income_pc: Petroleum, coal, natural gas, and metals production per capita. Real value of petroleum, coal, natural gas, and metals produced per capita.
v2peprisch: Primary school enrollment
v2pesecsch: Secondary school enrollment
v2petersch: Tertiary school enrollment

Variables on Education, Low- and Middle-level Technologies

In addition to school enrollment, I believe that the amount of educational resources provided by governments can have an impact on my dependent variables, diversity and number of patents in various technologies. Because education resource allocation clarifies government intervention (or investment) in the educational sector. Also, I include several variables that are associated with different levels of technology.

wdi_araland: Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.
wdi_empch: Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey.
wdi_empind: Employment in industry as a percentage of all employment. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water).
wdi_expedu: General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.
wdi_expedup: Expenditure on Primary education, expressed as a percentage of total general government expenditure on education. Divide government expenditure on a given level of education (ex. primary, secondary) by total government expenditure on education (all levels combined).
- A high percentage of government expenditure on education spent on a given level denotes a high priority given to that level compared to others. When interpreting this indicator, one should take into account enrollment at that level, and the relative costs per student between different levels of education.
wdi_expedus: Expenditure on Secondary education, expressed as a percentage of total general government expenditure on education. Divide government expenditure on a given level of education (ex. primary, secondary) by total government expenditure on education (all levels combined).
- A high percentage of government expenditure on education spent on a given level denotes a high priority given to that level compared to others. When interpreting this indicator, one should take into account enrollment at that level, and the relative costs per student between different levels of education.
wdi_expedut: Expenditure on Tertiary education, expressed as a percentage of total general government expenditure on education. Divide government expenditure on a given level of education (ex. primary, secondary) by total government expenditure on education (all levels combined).
- A high percentage of government expenditure on education spent on a given level denotes a high priority given to that level compared to others. When interpreting this indicator, one should take into account enrollment at that level, and the relative costs per student between different levels of education.
wdi_expstup: Government expenditure per student, primary (% of GDP per capita). Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the primary level of education, expressed as a percentage of GDP per capita.
wdi_expstus: Government expenditure per student, secondary (% of GDP per capita). Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the secondary level of education, expressed as a percentage of GDP per capita.
wdi_expstut: Government expenditure per student, tertiary (% of GDP per capita). Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given tertiary of education, expressed as a percentage of GDP per capita.
wdi_fdiin/wdi_fdiout: Foreign direct investment, net inflows or net outflows (% of GDP)

Potential Variables in Mind

The number of Ph.D. in a country and given year.
Comparable measurements of each different economic sectors (1st-2nd-3rd industrial stages)
World Development Indicator measurements have too many missings.

Variables Manipulation

1-year Lagged

All the explanatory variables take 1-year lagged values and I manipulate two lagged dependent variables for diversity and number. You can download primarily merged sample here. Below shows the first 100 rows of the sample data.

Preliminary Analyses

Simply, let us model explaining the number of patents of technologies. \(i\) is a state, \(t\) is a given year, and \(k\) is the level of technology \(\in \{\text{Low, Middle, High}\}\).

\[ \text{\# of Patents}_{i, t, k} \sim f(\text{Indoctrination}, \text{economic capacity}, \text{other covariates}) \]

\[ \text{Diversity}_{i, t, k}\sim f(\text{Indoctrination}, \text{economic capacity}, \text{other covariates}) \]

Hypothesis 1 without Ctrls

# Model for H1: Indoctrination leads to less advanced technological developments
#               because indoctrination emphasizes increasing citizens'
#               compliance with the regime.
library(estimatr)

lm_robust(log(sum_patent+1) ~ 
     lag_inco + as.factor(ccode) + as.factor(year), 
     se_type = "stata", 
   data = patent_indoc) -> h1m1

lm_robust(log(sum_patent+1) ~ 
     lag_cent + as.factor(ccode) + as.factor(year), 
     se_type = "stata",
   data = patent_indoc) -> h1m2

lm_robust(log(sum_patent+1) ~ 
     lag_inco + lag_lngdppc + as.factor(ccode) + as.factor(year), 
     se_type = "stata",
   data = patent_indoc) -> h1m3

lm_robust(log(sum_patent+1) ~ 
     lag_cent + lag_lngdppc + as.factor(ccode) + as.factor(year), 
     se_type = "stata",
   data = patent_indoc) -> h1m4

lm_robust(log(sum_patent+1) ~ 
     lag_inco*lag_lngdppc + as.factor(ccode) + as.factor(year), 
     se_type = "stata",
   data = patent_indoc) -> h1m5

lm_robust(log(sum_patent+1) ~ 
     lag_cent*lag_lngdppc + as.factor(ccode) + as.factor(year), 
     se_type = "stata",
   data = patent_indoc) -> h1m6

Indoctrination, Economic Capacity, and Intellectual Property Development
(DV = # of patents in general / without ctrls / with robust clustered SE)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Coherent Indoctrination	-1.21^***		-1.29^***		-0.63
	(0.15)		(0.15)		(0.34)
Centralized Education		-0.51^***		-0.58^***		-0.24
		(0.13)		(0.13)		(0.27)
Coherence×Ln(GDPpc)					-0.29^*
					(0.13)
Centralization×Ln(GDPpc)						-0.17
						(0.12)
Ln(GDPpc)			0.61^***	0.62^***	0.80^***	0.74^***
			(0.05)	(0.05)	(0.10)	(0.10)
(Constant)	9.50^***	9.48^***	7.29^***	7.21^***	6.53^***	6.75^***
	(0.07)	(0.07)	(0.21)	(0.21)	(0.39)	(0.39)
Country Fixed Effects	✓	✓	✓	✓	✓	✓
Year Fixed Effects	✓	✓	✓	✓	✓	✓
R²	0.95	0.95	0.95	0.95	0.95	0.95
Adj. R²	0.95	0.95	0.95	0.95	0.95	0.95
Num. obs.	10559	10559	9768	9768	9768	9768
RMSE	0.65	0.65	0.64	0.64	0.64	0.64
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Hypothesis 1 with Ctrls

# Model for H1: Indoctrination leads to less advanced technological developments
#               because indoctrination emphasizes increasing citizens'
#               compliance with the regime.

var_list <- patent_indoc |> dplyr::select(contains("lag_")) |> names()

for (i in 1:8) {
  manual <- "log(sum_patent+1) ~ lag_inco + lag_lngdppc + as.factor(ccode) + as.factor(year)"
  add <- dput(var_list[c(9:11, 15, 16, 19, 23, 26)])
  myformula <- as.formula(paste(manual, "+",  add[i], collapse =  "+"))
  assign(paste0("h1", "ctrl", i),
         lm_robust(myformula, 
                   se_type = "stata", 
   data = patent_indoc))
  }

Indoctrination, Economic Capacity, and Intellectual Property Development
(DV = # of patents in general / Cohensive Indoctrination with ctrls & robust clustered SE)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Coherent Indoctrination	-1.70^***	-1.64^***	-1.34^***	-1.31^***	-2.01^***	-1.81^***	-0.43^*	-0.64^**
	(0.15)	(0.16)	(0.15)	(0.15)	(0.23)	(0.27)	(0.22)	(0.24)
Ln(GDPpc)	0.61^***	0.62^***	0.62^***	0.63^***	0.35^**	0.78^***	1.01^***	1.00^***
	(0.05)	(0.05)	(0.05)	(0.05)	(0.11)	(0.14)	(0.10)	(0.12)
Electoral Democracy	-0.90^***
	(0.11)
Liberal Democracy		-0.73^***
		(0.12)
Democracy			-0.15^***
			(0.03)
Population				0.18^*
				(0.08)
Resource Dependence					0.03
					(0.07)
Tertiary School Enrollment						0.01^***
						(0.00)
Tertiary Worker (% Employee)							0.01^***
							(0.00)
Govt exp. per student, tertiary								0.00^***
								(0.00)
(Constant)	8.04^***	7.83^***	7.37^***	5.39^***	8.43^***	5.88^***	5.36^***	6.51^***
	(0.22)	(0.22)	(0.21)	(0.87)	(0.43)	(0.49)	(0.37)	(0.46)
Country Fixed Effects	✓	✓	✓	✓	✓	✓	✓	✓
Year Fixed Effects	✓	✓	✓	✓	✓	✓	✓	✓
R²	0.95	0.95	0.95	0.95	0.96	0.97	0.96	0.97
Adj. R²	0.95	0.95	0.95	0.95	0.96	0.97	0.96	0.97
Num. obs.	9768	9744	9699	9768	4538	4265	4309	3506
RMSE	0.63	0.64	0.64	0.64	0.54	0.57	0.54	0.49
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Coherent indoctrination significantly decreases the number of patents while economic capacity increases the patent numbers. Holding these two main explanatory variables constant, I establish eight models, including individual control variables as well as the main explanatory variables. Thus, these eight models show the effect of added control variable on the dependent variable when we hold the main explanatory variables constant.

Model 1: One unit increase of electoral democracy index is associated with -0.9 percent point decrease in the number of patents on average.
Model 2: One unit increase of liberal democracy index is associated with -0.734 percent point decrease in the number of patents on average.
Model 3: Democracies have -0.153 percent point less patents than non-democracies on average.
Model 4: One unit increase in population is associated with 0.177 percent point increase in the number of patents on average.
Model 6: One unit increase in tertiary school enrollment is associated with 0.012 percent point increase in the number of patents on average.
Model 7: One unit increase in proportion of tertiary workers in the total employee is associated with 0.01 percent point increase in the number of patents on average.
Model 8: One unit increase in proportion of government expenditure per student in the tertiary education is associated with 0.001 percent point increase in the number of patents on average.

# Model for H1: Indoctrination leads to less advanced technological developments
#               because indoctrination emphasizes increasing citizens'
#               compliance with the regime.
lm_robust(
  log(sum_patent+1) ~ 
    lag_inco + lag_lngdppc + 
    lag_edi + lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full1

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco + lag_lngdppc + 
    lag_libdem + lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full2

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco + lag_lngdppc + 
    lag_regime + lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full3

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco*lag_lngdppc + 
    lag_edi + lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full4

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco*lag_lngdppc + 
    lag_libdem + lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full5

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco*lag_lngdppc + 
    lag_regime + lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full6

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco*lag_edi + lag_lngdppc + 
    lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full7

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco*lag_libdem + lag_lngdppc + 
    lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full8

lm_robust(
  log(sum_patent+1) ~ 
    lag_inco*lag_regime + lag_lngdppc + 
    lag_pop + 
    lag_resrev + lag_tersch + 
    lag_advterwork + lag_stuterexp +
    as.factor(ccode) + as.factor(year), 
                   se_type = "stata", 
   data = patent_indoc) -> h1full9

Indoctrination, Economic Capacity, and Intellectual Property Development
(DV = # of patents in general / Cohensive Indoctrination with interactions ctrls
& robust clustered SE)
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
Coherent Indoctrination	-0.75	-0.77	-0.82	-11.65^***	-11.91^***	-12.38^***	-14.70^***	-13.91^***	-0.37
	(0.48)	(0.48)	(0.49)	(2.08)	(2.08)	(2.08)	(2.66)	(2.61)	(1.28)
Ln(GDPpc)	-1.16^**	-1.17^**	-1.07^*	-3.07^***	-3.14^***	-3.12^***	-1.04^*	-1.35^**	-1.07^*
	(0.43)	(0.44)	(0.43)	(0.59)	(0.60)	(0.59)	(0.43)	(0.43)	(0.43)
Electoral Democracy	-1.47^**			-1.40^**			-10.03^***
	(0.51)			(0.52)			(1.63)
Liberal Democracy		-1.71^**			-1.77^**			-10.94^***
		(0.62)			(0.64)			(1.85)
Democracy			-0.24^*			-0.30^**			0.00
			(0.10)			(0.10)			(0.73)
Coherence×Ln(GDppc)				3.27^***	3.34^***	3.46^***
				(0.59)	(0.59)	(0.59)
Coherence×EDI							16.30^***
							(2.96)
Coherence×Liberal Democracy								16.96^***
								(3.19)
Coherence×Democracy(=1)									-0.46
									(1.24)
Population	4.86^***	4.90^***	4.99^***	4.48^***	4.51^***	4.60^***	4.48^***	4.54^***	5.02^***
	(1.00)	(1.01)	(1.01)	(0.95)	(0.95)	(0.96)	(0.98)	(0.98)	(1.02)
Resource Dependence	0.71	0.71	0.69	0.91	0.93	0.92	0.65	0.71	0.70
	(0.61)	(0.61)	(0.61)	(0.58)	(0.58)	(0.58)	(0.60)	(0.60)	(0.61)
Tertiary School Enrollment	0.01	0.01	0.01	0.01^*	0.01^*	0.01^*	0.00	0.01	0.01
	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
Tertiary Worker (% Employee)	-0.02^**	-0.02^**	-0.02^**	-0.02^*	-0.02^*	-0.02^**	-0.01	-0.01^*	-0.02^**
	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Govt exp. per student, tertiary	0.00^***	0.00^***	0.00^***	0.00^***	0.00^***	0.00^***	0.00^***	0.00^***	0.00^***
	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
(Constant)

Country Fixed Effects	✓	✓	✓	✓	✓	✓	✓	✓	✓
Year Fixed Effects	✓	✓	✓	✓	✓	✓	✓	✓	✓
R²	0.98	0.98	0.98	0.98	0.98	0.98	0.98	0.98	0.98
Adj. R²	0.98	0.98	0.98	0.98	0.98	0.98	0.98	0.98	0.98
Num. obs.	1006	1006	1006	1006	1006	1006	1006	1006	1006
RMSE	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.39	0.39
^*p < 0.001; ^p < 0.01; ^*p < 0.05

# Model for H2a: Indoctrination increases low-level technological developments while decreasing high-level technological developments.

# Model for H2b: Indoctrination increases technological developments, but decreases diversity of technological developments.

# Model for H3: The effect of indoctrination on technological developments is conditional on the economic capacity of a state.

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<<<<<<< Updated upstream =======

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